Jaller, Miguel

  • Assistant Professor, Civil & Environmental Engineering
Contact

(530) 752-7062
mjaller@ucdavis.edu
2001 Ghausi Hall, 3143

Research Interests

  • Industrial and Transportation Engineering
  • Sustainable Transportation Systems
  • Humanitarian Logistics
  • Supply Chain Management
  • Operations Research

Biography

Miguel Jaller’s research interests are in the areas of freight transportation, sustainable transportation systems, and humanitarian logistics. He has published scientific and technical publications on these topics, and has presented at different national and international venues. Dr. Jaller has been a part of several important research and consulting projects funded …

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Miguel Jaller’s research interests are in the areas of freight transportation, sustainable transportation systems, and humanitarian logistics. He has published scientific and technical publications on these topics, and has presented at different national and international venues. Dr. Jaller has been a part of several important research and consulting projects funded by the United States Department of Transportation, the New York Metropolitan Transportation Council, the National Cooperative Freight Research Program, the National Science Foundation, and the Inter-American Development Bank, among others.

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2013 Best Paper Award from the College of Humanitarian Operations and Crisis Management, 24th Production and Operations Management Society (POMS) Conference for the paper “Locating Points of Distribution in Large Urban Disasters.”

2011 Thomas Archibald Bedford Prize from Rensselaer Polytechnic Institute

2006-2011 Research Assistantship Award from Rensselaer Polytechnic Institute

ECI 251 Transportation Demand Analysis (4)

Lecture—4 hours. Prerequisite: course 114 or the equivalent. Procedures used in urban travel demand forecasting. Principles and assumptions of model components (trip generation, trip distribution, model split). New methods of estimating travel demand. Computer exercises using empirical data to calibrate models and forecast travel demand.